Empowering catastrophic far-forward self-care: Nobody should die alone without trying
Bibliographic record
Abstract
LAY SUMMARY Traumatic injury is the most common cause of death among young people. Most victims of trauma die alone before medical response is possible. Typical causes of death are not overly complex to fix if access to standard hospital interventions is feasible. Dying victims are often connected to smartphone-supporting informatic communication technologies, which make available a worldwide network of experts who can potentially reassure and remotely diagnose victims and provide life-saving advice. TeleMentored Ultrasound Supported Medical Interventions (TMUSMI) researchers have focused on empowering point-of-care providers to perform outside their scope and deliver life-saving interventions. With the recognition that COVID-19 has profoundly isolated many people, solutions to respect COVID-19 isolation policies have stimulated the TMUSMI group to appreciate the potential for informatic technologies’ effect on the ability to care for oneself in cases of catastrophic injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".